Prediction Of Deterioration Research Articles

A hybrid spherical fuzzy AHP-MARCOS model for evaluating the condition of saltwater pipes in Hong Kong

PurposeWater pipes degrade over time for a variety of pipe-related, soil-related, operational, and environmental factors. Hence, municipalities are necessitated to implement effective maintenance and rehabilitation strategies for water pipes based on reliable deterioration models and cost-effective inspection programs. In the light of foregoing, the paramount objective of this research study is to develop condition assessment and deterioration prediction models for saltwater pipes in Hong Kong.Design/methodology/approach As a perquisite to the development of condition assessment models, spherical fuzzy analytic hierarchy process (SFAHP) is harnessed to analyze the relative importance weights of deterioration factors. Afterward, the relative importance weights of deterioration factors coupled with their effective values are leveraged using the measurement of alternatives and ranking according to the compromise solution (MARCOS) algorithm to analyze the performance condition of water pipes. A condition rating system is then designed counting on the generalized entropy-based probabilistic fuzzy C means (GEPFCM) algorithm. A set of fourth order multiple regression functions are constructed to capture the degradation trends in condition of pipelines overtime covering their disparate characteristics.FindingsAnalytical results demonstrated that the top five influential deterioration factors comprise age, material, traffic, soil corrosivity and material. In addition, it was derived that developed deterioration models accomplished correlation coefficient, mean absolute error and root mean squared error of 0.8, 1.33 and 1.39, respectively.Originality/valueIt can be argued that generated deterioration models can assist municipalities in formulating accurate and cost-effective maintenance, repair and rehabilitation programs.

State-Based Technical Condition Assessment and Prediction of Concrete Box Girder Bridges

The technical condition of bridges has become a crucial issue for organizing the maintenance and repairs in bridge management systems. It is of great practical engineering significance to construct an effective model for predicting the technical condition degradation of the bridge through the use of the historical inspection data. Based on the semi-Markov random process, this paper proposes a useful deterioration prediction model for bridges in the highway network. From the historical inspection data of the prefabricated concrete box girder bridges, the degradation curves of technical condition rating are obtained. The effect of bridge length on degradation rate of the prefabricated concrete box girder bridges is analyzed. According to the Weibull distribution parameters of different condition grades, the technical state degradation models for a bridge group and an individual bridge are proposed to predict the performance of the overall bridge and superstructure of the bridge. The results show that with the increase in bridge length, the degradation rate of bridge technical condition increases. The degradation rate of the technical condition of the superstructure is faster than that of the overall bridge. The proposed semi-Markov stochastic degradation model for the bridge group can not only predict the different condition ratings of the bridges at any time, but also predict the future deterioration trend of an individual bridge under any ratings.

Along with PaO2/FiO2 ratio and lymphopenia, low HLA-DR monocytes are the only additional parameter that independently predicts the clinical course of undifferentiated SARS-CoV-2 patients in emergency departments.

Because one-third of patients deteriorate after their admission to the emergency department, assessing the prognosis of COVID-19 patients is of great importance. However, to date, only lymphopenia and the partial pressure of oxygen/fraction of inspired oxygen (PaO2/FiO2) ratio have been reported as partly predictive of COVID-19-related further deterioration, and their association has not been evaluated. We asked whether other key biomarkers of SARS-CoV-2 immunologic defects-increase in circulating immature granulocytes, loss of monocyte HLA-DR (mHLA-DR) expression, and monocyte differentiation blockade-could also predict further COVID-19 deterioration. A series of 284 consecutive COVID-19 patients, with the sole inclusion criterion of being an adult, were prospectively enrolled at emergency department admission (day 0) of 2 different hospitals: 1 for the exploratory cohort (180 patients) and 1 for the confirmatory cohort (104 patients). Deterioration was assessed over the next 7 days. Neither increased immature granulocyte levels nor monocyte differentiation blockade predicted patient worsening. Among more than 30 clinical, biological, and radiological parameters, the value of decreased P/F ratio and lymphopenia for prediction of further COVID-19 deterioration was strongly confirmed, and the loss of mHLA-DR was the only additional independent marker. Combined together in a simple OxyLymphoMono score, the 3 variables perfectly predicted patients who did not worsen and correctly predicted worsening in 59% of cases. By highlighting lymphocyte and monocyte defects as preceding COVID-19 deterioration, these results point on early immunosuppression in COVID-19 deterioration. Combining P/F ratio, lymphopenia, and loss of mHLA-DR together in a simple and robust score could offer a pragmatic method for COVID-19 patient stratification.

Early prediction of environmental deterioration in a coastal fish farming area using lactic acid bacteria as an indicator

Early prediction of environmental deterioration in a coastal fish farming area using lactic acid bacteria as an indicator

A hybrid Bayesian optimization-based deep learning model for modeling the condition of saltwater pipes in Hong Kong

The water distribution network is one of the most pivotal services for communities, and that’s why its effective and secure operation is crucial to the growth of national and global economies. To this end, the paramount objective of this study is to devise an automated self-adaptive model for deterioration prediction of saltwater pipes. The developed model is envisioned on coupling deep learning network with Bayesian optimization (HBO-DL) for forecasting the condition of different material categories of saltwater pipes stepping on their pip-related, soil-related, operational-related, and environmental-related features. In this regard, Bayesian optimization is leveraged to amplify the training mechanism of deep learning neural network through iterative optimization of its hyper parameters. The developed model is validated through several folds of validation that encompass performance evaluation, statistical analysis, graphical comparison, and unified ranking. The conducted comparative analysis evinced that the developed HBO-DL model managed to significantly perform better than feed forward neural network, support vector machines and Gaussian process regression by 76.85%, 73.31% and 79.08%, respectively. The developed prediction model can stand as a practical and useful tool to forecast the condition performance of water networks which aids municipalities in designing optimum intervention plans evading socioeconomic losses elicited from pipe failures.

Prediction of abrasive wear and surface hardness of printed parts by SLA technology

In the present study, a prediction of hardness deterioration and abrasive wear was performed through a neural network using artificial intelligence on a material printed in SLA. This article aims to predict the mechanical properties, wear resistance and surface hardness of parts manufactured by SLA stereolithography printing. A full factorial DOE was used to associate the peculiar parameters (print orientation, cure time, layer height) to perform experiments. The mechanical properties were evaluated according to ASTM regulations, with the objective of obtaining feeding data and validation of the predictions of the Taber Wear Index and hardness using an artificial neural network. The experimental results are in good agreement with the measured data with satisfactory prediction errors with a mean square error (MSE) of 0.01 corresponding to abrasive wear using the clear resin and a mean absolute error (MSE) of 0.09 with an R2 of 0.756, the prediction with the neural network with a mean square error (MSE) of 2.47 corresponding to abrasive wear using the tough resin and a mean absolute error (MSE) of 14.3 with an R2 of 0.97. It was shown that the accuracy of the prediction is reasonable, and the network has the potential to be improved if the experimental database for training the network could be expanded. Therefore, wear and hardness mechanical properties can be predicted appropriately with an ANN.

Prediction of early neurologic deterioration in patients with perforating artery territory infarction using machine learning: a retrospective study.

Early neurological deterioration (END) is a frequent complication in patients with perforating artery territory infarction (PAI), leading to poorer outcomes. Therefore, we aimed to apply machine learning (ML) algorithms to predict the occurrence of END in PAI and investigate related risk factors. This retrospective study analyzed a cohort of PAI patients, excluding those with severe stenosis of the parent artery. We included demographic characteristics, clinical features, laboratory data, and imaging variables. Recursive feature elimination with cross-validation (RFECV) was performed to identify critical features. Seven ML algorithms, namely logistic regression, random forest, adaptive boosting, gradient boosting decision tree, histogram-based gradient boosting, extreme gradient boosting, and category boosting, were developed to predict END in PAI patients using these critical features. We compared the accuracy of these models in predicting outcomes. Additionally, SHapley Additive exPlanations (SHAP) values were introduced to interpret the optimal model and assess the significance of input features. The study enrolled 1,020 PAI patients with a mean age of 60.46 (range 49.11-71.81) years. Of these, 30.39% were women, and 129 (12.65%) experienced END. RFECV selected 13 critical features, including blood urea nitrogen (BUN), total cholesterol (TC), low-density-lipoprotein cholesterol (LDL-C), apolipoprotein B (apoB), atrial fibrillation, loading dual antiplatelet therapy (DAPT), single antiplatelet therapy (SAPT), argatroban, the basal ganglia, the thalamus, the posterior choroidal arteries, maximal axial infarct diameter (measured at < 15 mm), and stroke subtype. The gradient-boosting decision tree had the highest area under the curve (0.914) among the seven ML algorithms. The SHAP analysis identified apoB as the most significant variable for END. Our results suggest that ML algorithms, especially the gradient-boosting decision tree, are effective in predicting the occurrence of END in PAI patients.

Modelos probabilísticos de previsão da deterioração de pontes baseados em matrizes de Markov, utilizando dados reais e simulados de modelos determinísticos

Abstract This study uses real and simulated information from 885 bridges in Brazil. A total of 2,655 available inspection data were collected from the database, and 37,170 additional data were simulated from deterministic deterioration prediction models developed in previous studies. The probabilistic Markov matrices-based models obtained include one covering all the bridges, specific models for non-aggressive and aggressive environments, and models for Average Daily Traffic (ADT) of less than and more than 4,000. Validation showed good metrics, with a coefficient of determination of 0.6268, a mean absolute error and mean squared error below 0.5, and an accuracy of 66.25%. Finally, these tools enable more accurate forecasting, and a better understanding of the risks associated with the deterioration of structures for safe and cost-effective bridge management.

Service life prediction of timber-concrete composite floors

Timber-concrete composite (TCC) systems have application for new floors and for upgrading and enhancing of existing timber floors in residential and office buildings. In order to develop the optimal maintenance programs of structures, it is essential to predict the performance of structures over their life-cycle. The long-term behavior of timber-concrete composite floors is influenced by a combination of various factors, including material properties, load distribution, moisture effects, temperature effects and durability considerations. Timber and concrete material in the composite system, exhibit different behaviors over time due to their inherent characteristics. Deterioration prediction models are used to estimate the future degradation and condition of various types of structures. The aim of this paper is to present two probabilistic models, random variable deterioration rate model and stochastic gamma process model, that will capture uncertainty and variability associated with the deterioration of TCC floor under the service load and to estimate its service life.

Explainable hierarchical clustering for patient subtyping and risk prediction.

We present a pipeline in which machine learning techniques are used to automatically identify and evaluate subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. Patient clusters are determined using routinely collected hospital data, such as those used in the UK's National Early Warning Score 2 (NEWS2). An iterative, hierarchical clustering process was used to identify the minimum set of relevant features for cluster separation. With the use of state-of-the-art explainability techniques, the identified subtypes are interpreted and assigned clinical meaning, illustrating their robustness. In parallel, clinicians assessed intracluster similarities and intercluster differences of the identified patient subtypes within the context of their clinical knowledge. For each cluster, outcome prediction models were trained and their forecasting ability was illustrated against the NEWS2 of the unclustered patient cohort. These preliminary results suggest that subtype models can outperform the established NEWS2 method, providing improved prediction of patient deterioration. By considering both the computational outputs and clinician-based explanations in patient subtyping, we aim to highlight the mutual benefit of combining machine learning techniques with clinical expertise.

Predicting Deterioration from Wearable Sensor Data in People with Mild COVID-19

Coronavirus has caused many casualties and is still spreading. Some people experience rapid deterioration that is mild at first. The aim of this study is to develop a deterioration prediction model for mild COVID-19 patients during the isolation period. We collected vital signs from wearable devices and clinical questionnaires. The derivation cohort consisted of people diagnosed with COVID-19 between September and December 2021, and the external validation cohort collected between March and June 2022. To develop the model, a total of 50 participants wore the device for an average of 77 h. To evaluate the model, a total of 181 infected participants wore the device for an average of 65 h. We designed machine learning-based models that predict deterioration in patients with mild COVID-19. The prediction model, 10 min in advance, showed an area under the receiver characteristic curve (AUC) of 0.99, and the prediction model, 8 h in advance, showed an AUC of 0.84. We found that certain variables that are important to model vary depending on the point in time to predict. Efficient deterioration monitoring in many patients is possible by utilizing data collected from wearable sensors and symptom self-reports.

Data-driven prediction of possible quality deterioration in injection molding processes

We propose an approach for the automated prediction of possible quality deteriorations at injection molding machines using data-driven models. This approach relies on data solely recorded during the regular production phase without the need to (i.) collect data from anomalous phases, which may be cost-intensive due to production waste, (ii.) perform time-intensive (manual) annotation cycles for assigning class labels, and (iii.) know specific fault modes in advance. Our approach embeds two main concepts: the first is the establishment of causal relations between process variables, which serve as online residual generators. The multi-variate residual signals are analyzed using an advanced independent component analysis, whose reconstruction characteristics serve as a control signal indicating a potential anomaly of any type/mode, upon violation of the internal dependence structure. No parameters are needed for the online analysis, which in turn avoids the use of (pre-collected) data from anomalous phases, which is typically required to tune the parameters properly. The second concept performs a direct prediction of quality criteria, which are permanently by-measured through the usage of an optical inspection system of the produced/molded parts. Time series-based trends of the process data are used to establish quality prediction models based on a specific time series transformation technique. It preserves the local structure of the data to ensure it can be effectively combined with predictive fuzzy systems training. To account for system dynamics, the prior trained prediction models can be automatically updated on the fly with new measurements. To further increase flexibility in the case of higher dynamics, as was the case in our application, a dynamic lazy learning (dLL) approach was developed in combination with partial least squares. This technique is able to permanently update the reference data base and establish a local model per new query sample with reduced input dimensionality. Our approach was successfully evaluated during the real injection molding processes of bottle caps: several real-occurring anomalies and significant changes in material and production parameters were successfully found over a horizon of 7162 production cycles. Furthermore, three essential quality criteria were predicted with a correlation of 0.7, 0.89 and 0.98 between predicted and observed trend lines, with mean absolute error rates below 5%. This makes the detection of quality deteriorations possible at an early stage.

Machine Learning Models Versus the National Early Warning Score System for Predicting Deterioration: Retrospective Cohort Study in the United Arab Emirates

Background Early warning score systems are widely used for identifying patients who are at the highest risk of deterioration to assist clinical decision-making. This could facilitate early intervention and consequently improve patient outcomes; for example, the National Early Warning Score (NEWS) system, which is recommended by the Royal College of Physicians in the United Kingdom, uses predefined alerting thresholds to assign scores to patients based on their vital signs. However, there is limited evidence of the reliability of such scores across patient cohorts in the United Arab Emirates. Objective Our aim in this study was to propose a data-driven model that accurately predicts in-hospital deterioration in an inpatient cohort in the United Arab Emirates. Methods We conducted a retrospective cohort study using a real-world data set that consisted of 16,901 unique patients associated with 26,073 inpatient emergency encounters and 951,591 observation sets collected between April 2015 and August 2021 at a large multispecialty hospital in Abu Dhabi, United Arab Emirates. The observation sets included routine measurements of heart rate, respiratory rate, systolic blood pressure, level of consciousness, temperature, and oxygen saturation, as well as whether the patient was receiving supplementary oxygen. We divided the data set of 16,901 unique patients into training, validation, and test sets consisting of 11,830 (70%; 18,319/26,073, 70.26% emergency encounters), 3397 (20.1%; 5206/26,073, 19.97% emergency encounters), and 1674 (9.9%; 2548/26,073, 9.77% emergency encounters) patients, respectively. We defined an adverse event as the occurrence of admission to the intensive care unit, mortality, or both if the patient was admitted to the intensive care unit first. On the basis of 7 routine vital signs measurements, we assessed the performance of the NEWS system in detecting deterioration within 24 hours using the area under the receiver operating characteristic curve (AUROC). We also developed and evaluated several machine learning models, including logistic regression, a gradient-boosting model, and a feed-forward neural network. Results In a holdout test set of 2548 encounters with 95,755 observation sets, the NEWS system achieved an overall AUROC value of 0.682 (95% CI 0.673-0.690). In comparison, the best-performing machine learning models, which were the gradient-boosting model and the neural network, achieved AUROC values of 0.778 (95% CI 0.770-0.785) and 0.756 (95% CI 0.749-0.764), respectively. Our interpretability results highlight the importance of temperature and respiratory rate in predicting patient deterioration. Conclusions Although traditional early warning score systems are the dominant form of deterioration prediction models in clinical practice today, we strongly recommend the development and use of cohort-specific machine learning models as an alternative. This is especially important in external patient cohorts that were unseen during model development.

Bridge Condition Deterioration Prediction Using the Whale Optimization Algorithm and Extreme Learning Machine

To address the problem in model computations and the limited accuracy of current bridge deterioration prediction methods, this paper proposes a novel bridge deterioration prediction meth-od using the whale optimization algorithm and extreme learning machine (WOA-ELM). First, we collected a dataset consisting of 539 sets of bridge inspection data and determined the necessary influencing factors through correlation analysis. Subsequently, the WOA-ELM algorithm was applied to establish a nonlinear mapping relationship between each influencing factor and the bridge condition indicators. Furthermore, the extreme learning machine (ELM), back-propagation neural network (BPNN), decision trees (DT), and support vector machine (SVM) were employed for comparison to validate the superiority of the proposed method. In addition, this paper provides further substantiation of the model’s exceptional predictive capabilities across diverse bridge components. The results demonstrate the accurate predictive capability of the proposed method for bridge conditions. Compared with ELM, BPNN, DT, and SVM, the proposed method exhibits significant improvements in predictive accuracy, i.e., the correlation coefficient is increased by 4.1%, 11.4%, 24.5%, and 33.6%, and the root mean square error is reduced by 7.3%, 18.0%, 14.8%, and 18.1%, respectively. Moreover, the proposed method presents considerably enhanced generalization capabilities, resulting in the reduction in mean relative error by 11.6%, 15.3%, 6%, and 16.2%. The proposed method presents a robust framework for proactive bridge maintenance.

ASENN: attention-based selective embedding neural networks for road distress prediction

This study proposes an innovative neural network framework, ASENN (Attention-based Selective Embedding Neural Network), for the prediction of pavement deterioration. Considering the complexity and uncertainty associated with the pavement deterioration process, two fundamental frameworks, SEL (Selective Embedding Layer) and MDAL (Multi-Dropout Attention Layer), are combined to enhance feature abstraction and prediction accuracy. This approach is significant while analyzing the pavement deterioration process due to the high variability of the contributing deterioration factors. These factors, represented as tabular data, undergo filtering, embedding, and fusion stages in the SEL, to extract crucial features for an effective representation of pavement deterioration. Further, multiple attention-weighted combinations of raw data are obtained through the MDAL. Several SELs and MDALs were combined as basic cells and layered to form an ASENN. The experimental results demonstrate that the proposed model outperforms existing tabular models on four road distress parameter datasets corresponding to cracking, deflection, international roughness index, and rutting. The optimal number of cells was determined using different ablation settings. The results also show that the feature learning capabilities of the ASENN model improved as the number of cells increased; however, owing to the limited combination space of feature fields, extreme depths were not preferred. Furthermore, the ablation investigation demonstrated that MDAL can improve performance, particularly on the cracking dataset. Notably, compared with mainstream transformer models, ASENN requires significantly less storage and achieves faster execution speed.

Multidimensional dynamic prediction model for hospitalized patients with the omicron variant in China

PurposeTo establish dynamic prediction models by machine learning using daily multidimensional data for coronavirus disease 2019 (COVID-19) patients. MethodsHospitalized COVID-19 patients at Peking Union Medical College Hospital from Nov 2nd, 2022, to Jan 13th, 2023, were enrolled in this study. The outcome was defined as deterioration or recovery of the patient's condition. Demographics, comorbidities, laboratory test results, vital signs, and treatments were used to train the model. To predict the following days, a separate XGBoost model was trained and validated. The Shapley additive explanations method was used to analyze feature importance. ResultsA total of 995 patients were enrolled, generating 7228 and 3170 observations for each prediction model. In the deterioration prediction model, the minimum area under the receiver operating characteristic curve (AUROC) for the following 7 days was 0.786 (95% CI 0.721–0.851), while the AUROC on the next day was 0.872 (0.831–0.913). In the recovery prediction model, the minimum AUROC for the following 3 days was 0.675 (0.583–0.767), while the AUROC on the next day was 0.823 (0.770–0.876). The top 5 features for deterioration prediction on the 7th day were disease course, length of hospital stay, hypertension, and diastolic blood pressure. Those for recovery prediction on the 3rd day were age, D-dimer levels, disease course, creatinine levels and corticosteroid therapy. ConclusionThe models could accurately predict the dynamics of Omicron patients’ conditions using daily multidimensional variables, revealing important features including comorbidities (e.g., hyperlipidemia), age, disease course, vital signs, D-dimer levels, corticosteroid therapy and oxygen therapy.

Individualized Prediction of Task Performance Decline Using Pre-Task Resting-State Functional Connectivity.

As a common complaint in contemporary society, mental fatigue is a key element in the deterioration of the daily activities known as time-on-task (TOT) effect, making the prediction of fatigue-related performance decline exceedingly important. However, conventional group-level brain-behavioral correlation analysis has the limitation of generalizability to unseen individuals and fatigue prediction at individual-level is challenging due to the significant differences between individuals both in task performance efficiency and brain activities. Here, we introduced a cross-validated data-driven analysis framework to explore, for the first time, the feasibility of utilizing pre-task idiosyncratic resting-state functional connectivity (FC) on the prediction of fatigue-related task performance degradation at individual level. Specifically, two behavioral metrics, namely ∆RT (between the most vigilant and fatigued states) and TOTslope over the course of the 15-min sustained attention task, were estimated among three sessions from 37 healthy subjects to represent fatigue-related individual behavioral impairment. Then, a connectome-based prediction model was employed on pre-task resting-state FC features, identifying the network-related differences that contributed to the prediction of performance deterioration. As expected, prominent populational TOT-related performance declines were revealed across three sessions accompanied with substantial inter-individual differences. More importantly, we achieved significantly high accuracies for individualized prediction of both TOT-related behavioral impairment metrics using pre-task neuroimaging features. Despite the distinct patterns between both behavioral metrics, the identified top FC features contributing to the individualized predictions were mainly resided within/between frontal, temporal and parietal areas. Overall, our results of individualized prediction framework extended conventional correlation/classification analysis and may represent a promising avenue for the development of applicable techniques that allow precaution of the TOT-related performance declines in real-world scenarios.

Multi-time Step Deterioration Prediction of Freeways Using Linear Regression and Machine Learning Approaches: A Case Study

AbstractMulti-time step deterioration prediction of road pavements can provide more options for effective maintenance and rehabilitation decision under limited resources as compared to single time step prediction. This paper presents the 1–4 time step ahead prediction of the pavement cracking, rutting and roughness using their past values together with climate and traffic data as model inputs. Three prediction models were adopted, including the simple multiple linear regression (MLR) model and two sophisticated machine learning models, namely, support vector regression (SVR) and genetic programming (GP) models. An industry dataset of spray seal freeways is used to demonstrate the application of the methodology developed in this study. After calibration with observed data, the three prediction models are tested with unseen datasets using three performance indicators, namely, mean squared error (MSE), mean absolute percentage error (MAPE) and coefficient of determination (R squared). Among many results, all three models are unable to predict cracking with acceptable prediction accuracy. On the other hand, the rutting and roughness can be predicted with relatively good accuracy up to 4 time steps ahead. The sensitivity analysis shows that roughness and rutting prediction depends significantly on their previous or lagged values and not on remaining inputs such as annual average daily traffic (AADT) and rainfall. The methodology developed in this study is also applied to another dataset of asphalt freeways, which have similar model inputs. Similar findings are found with this dataset of asphalt freeways to that of spray seal freeways. The simple MLR model can produce similar prediction performance to the sophisticated SVR and GP models for rutting and roughness, suggesting the use of the MLR model as a benchmark for any development of prediction models for pavement deterioration.

Prediction of early neurological deterioration in acute ischemic stroke patients treated with intravenous thrombolysis

A proportion of acute ischemic stroke (AIS) patients suffer from early neurological deterioration (END) within 24 hours following intravenous thrombolysis (IVT), which greatly increases the risk of poor prognosis of these patients. Therefore, we aimed to explore the predictors of early neurological deterioration of ischemic origin (ENDi) in AIS patients after IVT and develop a nomogram prediction model. This study collected 244 AIS patients with post-thrombolysis ENDi as the derivation cohort and 155 patients as the validation cohort. To establish a nomogram prediction model, risk factors were identified by multivariate logistic regression analysis. The results showed that neutrophil to lymphocyte ratio (NLR) (OR 2.616, 95% CI 1.640–4.175, P < 0.001), mean platelet volume (MPV) (OR 3.334, 95% CI 1.351–8.299, P = 0.009), body mass index (BMI) (OR 1.979, 95% CI 1.285–3.048, P = 0.002) and atrial fibrillation (AF) (OR 8.012, 95% CI 1.341–47.873, P = 0.023) were significantly associated with ENDi. The area under the curve of the prediction model constructed from the above four factors was 0.981 (95% CI 0.961–1.000) and the calibration curve was close to the ideal diagonal line. Therefore, this nomogram prediction model exhibited good discrimination and calibration power and might be a reliable and easy-to-use tool to predict post-thrombolysis ENDi in AIS patients.

Probability-based damage thresholds for bridges’ inspection-based deterioration curves

A probability-based procedure is presented to define damage thresholds of bridges’ deterioration curves, which is applied to a large bridge database of real on-site inspections. The procedure is based on a change of variable to transform any distribution of bridge durability deterioration ratings into a normal distribution. This allows probability-based calculations of damage thresholds associated to certain criteria (e.g. Serviceability or Ultimate Limit State), for immediate application to deterioration prediction curves. This research defines a novel procedure for a probability-based analysis of inspection-based data and, de facto, provides a reliable statistical support system to traditional infrastructure management systems of bridges.